Agentic AI Engineering: Build Autonomous AI Workers (Python & LangChain)
Duration : 40 Hours
Embark on a transformative journey to unlock the potential of blockchain and hone practical skills that set you apart in today’s evolving world.
New to Web3
No coding background
Aspiring for Remarkable Growth
Prefers interactive learning
Desires practical experience
Prepare for Success
Covers the full stack from LLM foundation to secure, scalable deployment and monitoring (Source 4.1)
Program Eligibility
Software Developers, Data Scientists, MLOps Engineers, and Python Developers.
Prerequisite: Intermediate Python knowledge.
Agentic AI Engineering: Build Autonomous AI Workers (Python & LangChain)
27K+ Students Enrolled
AED 3000.00
+Taxes
Overview of Educity Programs
Course Schedule
- LLM Fundamentals: Core concepts of transformers, model training (fine-tuning vs. RAG), and optimizing models for latency and cost (e.g., using open-source models like Llama, Groq, or DeepSeek).
- The Agent Architecture: Deep dive into the core components: Planning, Memory (short-term & long-term), Tool-Use, and Reflection/Self-Correction.
- Prompt Engineering for Agents: Mastering advanced techniques like Chain-of-Thought (CoT), Tree-of-Thought (ToT), and using Pydantic for reliable, structured output from LLMs.
- Mastering CrewAI/LangGraph: Hands-on development of specialized multi-agent systems using the industry’s leading frameworks.
- Role-Based Collaboration: Defining roles, goals, and processes for collaborative agents (e.g., a Researcher Agent feeding information to a Writer Agent).
- Tool Creation & Function Calling: Equipping agents with custom tools (API access, database queries, web scraping) and implementing complex, multi-step function calling for real-world tasks.
- Advanced Flow Engineering: Designing dynamic, event-driven workflows using CrewAI Flows and LangGraph state machines for precise control and auditing of complex tasks.
- RAG Architecture Deep Dive: Moving beyond Naive RAG to implement advanced techniques like Query Transformation (e.g., Hypothetical Document Embeddings) and Reranking for high-quality context retrieval.
- Vector Database Implementation: Practical labs using production-grade vector databases (e.g., Pinecone, Qdrant, or Chroma) for high-speed, semantic search.
- Data Preparation: Mastering text chunking strategies, metadata filtering, and embedding model selection for optimal retrieval performance.
- Agentic RAG: Integrating RAG directly into the agent’s memory and reasoning process, allowing agents to autonomously decide when and how to retrieve external information.
- Containerization & Scaling: Packaging agents using Docker and deploying them reliably using Kubernetes or serverless functions for horizontal scaling.
- CI/CD for AI Agents: Setting up automated testing and deployment pipelines to manage agent versions and dependencies efficiently.
- Observability & Monitoring: Implementing logging, metrics, and tracing (LangSmith or equivalent) to monitor agent cost, latency, and model drift in production.
- Security & Compliance: Best practices for securing LLM API keys, managing user data, and auditing agent behavior for enterprise compliance and ethics.
Additional Activities
- The Promise: Stop writing code; start building autonomous workers. You will jump ahead of 90% of developers who are still focused only on basic LLM calls.
- Webinars and Workshops: Interactive sessions with industry experts.
- Networking Events: Opportunities to build connections with peers and professionals.
- Support Sessions: Guidance and assistance for queries and additional learning.
- Final Presentation: Present your capstone project to a panel of experts.
PAP and EMI Eligibility for 12 Months Educity Program:
Beyond Academics
Additional Activities Throughout the Program
Attain Recognition with the Certificate of Course Completion
Upon completing the course, you will receive a certificate—an impactful addition to your LinkedIn profile that can capture the interest of our hiring partners and prominent big data companies.